This research project develops scalable statistical models that (i) account for spatial uncertainty in various types of data and (ii) integrate many different databases collected at various space-time scales into a comprehensive analysis of bronchiolitis. This re- search is primarily motivated by health information on bronchiolitis from the US Military Health System (MHS). The MHS bronchiolitis database contains more than 140,000 bronchiolitis cases that have been spatially and temporally randomized (jittered) to ensure privacy. Hence, to properly utilize the MHS database to understand environmental factors influencing bronchiolitis abundance requires statistical methods that (i) account for the types of spatial uncertainty in the MHS data and (ii) combine multiple types of spatial data (e.g. air pollution and census data) on a large space-time scale.